1. Technical Field
An “RFID-Based Inference Platform” provides various techniques for enabling an enterprise intelligence system based on tracking of various interactions between users and objects, and in particular, to various techniques for using RFID tags in combination with other enterprise sensors and systems to track users and objects, infer their interactions, and provide these inferences for enabling further applications.
2. Background Art
Radio Frequency Identification (RFID) is a well-known electronic tagging technology that allows the detection and tracking of tags, and consequently any objects that they are affixed to. An RFID tag typically comprises a passive transponder that responds with identifying information when energized remotely by an RFID reader. This ability to do remote detection and tracking coupled with the low cost of passive tags has led to the widespread adoption of RFID in the supply chain world. RFID is used to track the movement of goods through a supply chain, whether it be pallets shipped between warehouses, cases delivered to stores, or items placed on the store shelves, thereby optimizing inventory management and yielding significant cost savings.
RFID tags come in three types: passive, active and semi-passive. Passive tags do not have any internal power supply. Instead, they use the electric current induced in the tag's antenna by the incoming RF signal from a reader to power the tag's circuitry and transmit a response back to the reader. Such tags typically have a read range from about 10 cm up to a few meters. Active tags, on the other hand, have their own internal battery to power the circuitry and transmit a response using an arbitrary RF technology such as WiFi. Due to their internal battery, active tags generally have a read range of hundreds of meters. Semi-passive tags have their own power supply to power their circuitry and to help with reception, but like passive tags, they use the RF induced current for transmitting a response back to the reader.
In particular, passive tags typically receive power through inductive or radiative coupling. Inductive coupling is used for powering LF (low frequency, 30-300 kHz) and HF (high frequency, 220 MHz) tags. Such tags receive power in the near field, which refers to the region within a few wavelengths of the reader's antenna. A reader antenna generates a magnetic field, inducing an electric current in the tag's antenna and charging a capacitor in the tag. Radiative coupling is used for UHF tags (Ultra High Frequency, above 100 MHz). In this case, the tag antenna receives signals and energy from the electromagnetic field emitted by the reader in the far field, the area beyond a few wavelengths.
RFID tags have been used for many purposes, including retail product identification and anti-theft devices. Further, RFID-based localization has been used to track tagged objects in and around RFID readers. Once such tracking systems uses a method to localize RFID tags using a mobile platform to automatically generate tag maps showing locations of objects such as people and robots in a general area. A related localization system provides techniques for finding locations of mostly static objects augmented with RFID tags, by iteratively refining observations made from multiple locations and in different directions by a mobile RFID reader, which is assumed to know its own location. This setup is integrated with a camera, which allows the image to be annotated with the estimated locations of RFID tagged objects and displayed in real time.
Several scenarios in ubiquitous computing require automatic inferencing of what a person is doing or intends to do. In the past, researchers have applied three main techniques to human-activity inference: computer vision, active sensor beacons, and passive RFID. While vision based inferencing techniques suffer from robustness and scalability problems, active sensor beacons require batteries. Approaches based on passive RFID tags avoid these difficulties, making them particularly attractive. Assuming that reliable detection of people-object interactions is possible, specific activities can be inferred from such interactions. For example, using a sufficient number of RFID tags, it can be detected that a person interacted with tea, water, and sugar. One possible inference from this interaction detection is that the person is trying to make tea. However, while conventional RFID inferencing techniques can provide rich information about such interactions, these conventional techniques tend to be either obtrusive, or require non-standard, customized RFID tags or devices (e.g., a special glove or bracelet to be worn by the user).
There has also been work on alternative, unobtrusive techniques for detecting interaction with RFID-tagged objects. One such technique uses variations in the response rate (i.e., the fraction of read attempts sent by the RFID reader that are responded to by an RFID tag) of individual tags and groups of tags to detect interaction. However, it has been observed that there is typically a sharp drop off in the response rate from 100% down to 0% depending upon range from the reader to the tags, making it hard to use response rate for detection.